code: def test_timegapsplit():
      cv = TimeGapSplit(
          date_serie=df["date"],
          train_duration=timedelta(days=5),
          valid_duration=timedelta(days=3),
          gap_duration=timedelta(days=0),
      )
  
      for i, indices in enumerate(cv.split(X_train, y_train)):
          train_mindate = df.loc[X_train.iloc[indices[0]].index][
              "date"
          ].min()
          train_maxdate = df.loc[X_train.iloc[indices[0]].index][
              "date"
          ].max()
          valid_mindate = df.loc[X_train.iloc[indices[1]].index][
              "date"
          ].min()
          valid_maxdate = df.loc[X_train.iloc[indices[1]].index][
              "date"
          ].max()
  
          assert (
              train_mindate
              <= train_maxdate
              <= valid_mindate
              <= valid_maxdate
          )
  
      # regression testing, check if output changes of the last fold
      assert train_mindate == datetime.datetime.strptime(
          "2018-01-16", "%Y-%m-%d"
      )
      assert train_maxdate == datetime.datetime.strptime(
          "2018-01-20", "%Y-%m-%d"
      )
      assert valid_mindate == datetime.datetime.strptime(
          "2018-01-21", "%Y-%m-%d"
      )
      assert valid_maxdate == datetime.datetime.strptime(
          "2018-01-23", "%Y-%m-%d"
      )
  
file_path: scikit-lego/tests/test_model_selection/test_timegapsplit.py
imports: import datetime
  from datetime import timedelta
  import numpy
  import pandas
  import pytest
  from sklearn.linear_model import Lasso
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import Pipeline
  from sklego.model_selection import TimeGapSplit
label_desc: Use image node_id 1 for calling a global function with example usage: test_timegapsplit() without return types